3 The relationship between governance, security, and responsible AI Together, data governance and security create the backbone for responsible AI use. High-quality, secure data ensures AI works ethically and effectively. Key areas to evaluate include data classification, access controls, encryption, incident response, and regulatory compliance. Access controls Data classification Classifying data is a critical part of These controls regulate who can access controlling how AI tools handle sensitive AI-relevant data and which applications or identities have permission to interact information. Typically, data and meetings with that data. Weak access controls can are classified as general, confidential, or lead to unauthorized exposure of sensitive highly confidential. Proper classification information, increasing the risk of breaches ensures that AI only accesses appropriate information, reducing the risk of exposing and misuse. sensitive data to unauthorized users. Data governance plays a key role in enforcing these controls by restricting Misclassification, on the other hand, can data access to authorized personnel lead to AI processing data that should be restricted, resulting in security breaches and specific applications, ensuring that sensitive data is handled appropriately. or compliance issues. Effective data governance, whether through automated This not only protects data integrity but tools or end-user policies, ensures that data also ensures that AI systems operate on secure, trusted datasets, enhancing their is classified correctly, safeguarding sensitive information and supporting the ability of AI reliability and compliance. to deliver reliable and compliant outputs. 11